Weir, R.F., et al.: Implantable myoelectric sensors (IMESs) for intramuscular electromyogram recording. IEEE Trans. Biomed. Eng. 56(1), 159-171

Biomechatronics Development Laboratory, Rehabilitation Institute of Chicago, Chicago, IL 60611, USA.
IEEE transactions on bio-medical engineering (Impact Factor: 2.35). 02/2009; 56(1):159-71. DOI: 10.1109/TBME.2008.2005942
Source: PubMed


We have developed a multichannel electrogmyography sensor system capable of receiving and processing signals from up to 32 implanted myoelectric sensors (IMES). The appeal of implanted sensors for myoelectric control is that electromyography (EMG) signals can be measured at their source providing relatively cross-talk-free signals that can be treated as independent control sites. An external telemetry controller receives telemetry sent over a transcutaneous magnetic link by the implanted electrodes. The same link provides power and commands to the implanted electrodes. Wireless telemetry of EMG signals from sensors implanted in the residual musculature eliminates the problems associated with percutaneous wires, such as infection, breakage, and marsupialization. Each implantable sensor consists of a custom-designed application-specified integrated circuit that is packaged into a biocompatible RF BION capsule from the Alfred E. Mann Foundation. Implants are designed for permanent long-term implantation with no servicing requirements. We have a fully operational system. The system has been tested in animals. Implants have been chronically implanted in the legs of three cats and are still completely operational four months after implantation.

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    • "Ultimately, such an implant would not require daily placement of the electrodes, making it easier to use than sEMG, while providing good quality and stable intramuscular signals, at the cost of requiring surgery for the implantation. In contrast to one of the few currently existing and tested wireless iEMG systems, such as IMES [16], the Myoplant system uses a centralized implant. It thereby allows separating recording locations from transmission location with the potential of optimizing the transmission location, minimizing the energy costs for power induction and simplifying the antenna configuration for data read-out. "
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    ABSTRACT: Objective: The ease of use and number of degrees of freedom of current myoelectric hand prostheses is limited by the information content and reliability of the surface electromyography (sEMG) signals used to control them. For example, cross-talk limits the capacity to pick up signals from small or deep muscles, such as the forearm muscles for distal arm amputations, or sites of targeted muscle reinnervation (TMR) for proximal amputations. Here we test if signals recorded from the fully implanted, induction-powered wireless Myoplant system allow long-term decoding of continuous as well as discrete movement parameters with better reliability than equivalent sEMG recordings. The Myoplant system uses a centralized implant to transmit broadband EMG activity from four distributed bipolar epimysial electrodes. Approach: Two Rhesus macaques received implants in their backs, while electrodes were placed in their upper arm. One of the monkeys was trained to do a cursor task via a haptic robot, allowing us to control the forces exerted by the animal during arm movements. The second animal was trained to perform a center-out reaching task on a touchscreen. We compared the implanted system with concurrent sEMG recordings by evaluating our ability to decode time-varying force in one animal and discrete reach directions in the other from multiple features extracted from the raw EMG signals. Main results: In both cases, data from the implant allowed a decoder trained with data from a single day to maintain an accurate decoding performance during the following months, which was not the case for concurrent surface EMG recordings conducted simultaneously over the same muscles. Significance: These results show that a fully implantable, centralized wireless EMG system is particularly suited for long-term stable decoding of dynamic movements in demanding applications such as advanced forelimb prosthetics in a wide range of configurations (distal amputations, TMR).
    Full-text · Article · Dec 2015 · Journal of Neural Engineering
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    • "First, the surface of the skin itself presents fundamental challenges to recording EMG signals from the underlying musculature. These limitations include: (1) susceptibility to electrical noise generated by the environment, (2) recording of electrical activity from other muscles adjacent to the electrode, thereby triggering unintended actions, (3) movement of the surface electrodes on the skin, especially with socket rotation, and (4) perspiration of the skin changing the electrical impedance (Weir et al., 2009). In addition, surface electrodes do not allow the simultaneous capture of multiple individual superficial and deep muscles of the forearm to control multiple degrees of freedom simultaneously. "
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    ABSTRACT: Advanced motorized prosthetic devices are currently controlled by EMG signals generated by residual muscles and recorded by surface electrodes on the skin. These surface recordings are often inconsistent and unreliable, leading to high prosthetic abandonment rates for individuals with upper limb amputation. Surface electrodes are limited because of poor skin contact, socket rotation, residual limb sweating, and their ability to only record signals from superficial muscles, whose function frequently does not relate to the intended prosthetic function. More sophisticated prosthetic devices require a stable and reliable interface between the user and robotic hand to improve upper limb prosthetic function.
    Preview · Article · Aug 2014 · Journal of Neuroscience Methods
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    • "Our results demonstrate the feasibility of achieving simultaneous , independent and continuous control of individual digits on a prostheses directly using extrinsic muscle EMG signals. These results open up promising possibilities for individuals with transradial amputations since there are a number of multi-digit prosthetic hands commercially available (like the Bebionic v2 by RSL Steeper or the iLIMB by Touch Bionics) and chronically implanted IMES [5] are becoming the reality [18], [19]. "
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    ABSTRACT: Restoring dexterous motor function equivalent to that of the human hand after amputation is one of the major goals in rehabilitation engineering. To achieve this requires the implementation of a effortless Human-Machine Interface that bridges the artificial hand to the sources of volition. Attempts to tap into the neural signals and to use them as control inputs for neuroprostheses range in invasiveness and hierarchical location in the neuromuscular system. Nevertheless today, the primary clinically viable control technique is the electromyogram measured peripherally by surface electrodes. This approach is neither physiologically appropriate nor dexterous because arbitrary finger movements or hand postures cannot be obtained. Here we demonstrate the feasibility of achieving real-time, continuous and simultaneous control of a multi-digit prosthesis directly from forearm muscles signals using intramuscular electrodes on healthy subjects. Subjects contracted physiologically appropriate muscles to control four degrees-of-freedom of the fingers of a physical robotic hand independently. Subjects described the control as intuitive and showed the ability to drive the hand into twelve postures without explicit training. This is the first study in which peripheral neural correlates were processed in real-time and used to control multiple digits of a physical hand simultaneously in an intuitive and direct way.
    Full-text · Article · Jan 2014 · IEEE transactions on neural systems and rehabilitation engineering: a publication of the IEEE Engineering in Medicine and Biology Society
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